Ensemble discriminative tracking utilizes a committee of classifiers, to
label data samples, which are in turn, used for retraining the tracker to
localize the target using the collective knowledge of the committee. Committee
members could vary in their features, memory update schemes, or training data,
however, it is inevitable to have committee members that excessively agree
because of large overlaps in their version space. To remove this redundancy and
have an effective ensemble learning, it is critical for the committee to
include consistent hypotheses that differ from one-another, covering the
version space with minimum overlaps. In this study, we propose an online
ensemble tracker that directly generates a diverse committee by generating an
efficient set of artificial training. The artificial data is sampled from the
empirical distribution of the samples taken from both target and background,
whereas the process is governed by query-by-committee to shrink the overlap
between classifiers. The experimental results demonstrate that the proposed
scheme outperforms conventional ensemble trackers on public benchmarks.

这篇研究论文提出了一个在线集成追踪器，通过生成一组有效的人工训练数据来直接生成一个多样化的委员会。人工数据是从目标和背景的样本经验分布中抽样，这个过程由委员会查询控制，以缩小分类器之间的重叠，实验结果表明，该方案在公共基准测试上胜过传统的集成追踪器。